{
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "# 导入必要的库\n",
        "import torch\n",
        "from diffusers import StableDiffusionPipeline\n",
        "from PIL import Image\n",
        "import os\n",
        "\n",
        "def generate_image_from_text(prompt: str, output_path: str = \"generated_image.png\", model_id: str = \"runwayml/stable-diffusion-v1-5\", device: str = \"cuda\", print_model_structure: bool = False):\n",
        "    \"\"\"\n",
        "    使用 Stable Diffusion 根据文本提示生成图像。\n",
        "\n",
        "    参数:\n",
        "    prompt (str): 用于生成图像的文本提示。\n",
        "    output_path (str): 生成图像的保存路径。\n",
        "    model_id (str): Hugging Face Hub 上的预训练模型 ID。\n",
        "                     一些流行的模型包括:\n",
        "                     - \"runwayml/stable-diffusion-v1-5\" (通用)\n",
        "                     - \"stabilityai/stable-diffusion-2-1\" (通用)\n",
        "                     - \"CompVis/stable-diffusion-v1-4\" (较早版本)\n",
        "                     - 还有许多针对特定风格微调的模型，例如 \"dreamlike-art/dreamlike-photoreal-2.0\"\n",
        "    device (str): 用于推理的设备 (\"cuda\" 表示 GPU, \"cpu\" 表示 CPU)。\n",
        "                  如果 CUDA 不可用，会自动切换到 CPU。\n",
        "    print_model_structure (bool): 是否打印模型的主要组件结构。\n",
        "    \"\"\"\n",
        "    print(f\"开始生成图像，提示: '{prompt}'\")\n",
        "    print(f\"使用模型: {model_id}\")\n",
        "\n",
        "    # 检查 CUDA 是否可用，否则使用 CPU\n",
        "    if device == \"cuda\" and not torch.cuda.is_available():\n",
        "        print(\"CUDA 不可用，切换到 CPU。生成过程可能会很慢。\")\n",
        "        device = \"cpu\"\n",
        "\n",
        "    print(f\"使用设备: {device}\")\n",
        "\n",
        "    try:\n",
        "        # 加载预训练的 Stable Diffusion pipeline\n",
        "        # torch_dtype=torch.float16 在 GPU 上可以加速并减少显存占用\n",
        "        # 如果在 CPU 上运行或 GPU 不支持 float16，可以移除 torch_dtype 参数或使用 torch.float32\n",
        "        pipe_args = {}\n",
        "        if device == \"cuda\":\n",
        "            pipe_args[\"torch_dtype\"] = torch.float16\n",
        "            # 如果显存较小，可以启用 VAE 分块处理以减少显存峰值\n",
        "            # pipe.enable_vae_slicing()\n",
        "            # 也可以启用模型 offloading，进一步减少显存占用，但会牺牲一些速度\n",
        "            # pipe.enable_model_cpu_offload()\n",
        "\n",
        "        print(\"正在加载模型... 这可能需要一些时间，特别是第一次运行时会自动下载模型。\")\n",
        "        pipe = StableDiffusionPipeline.from_pretrained(model_id, **pipe_args)\n",
        "        pipe = pipe.to(device)\n",
        "        print(\"模型加载完成。\")\n",
        "\n",
        "        # 如果设置了 print_model_structure 为 True，则打印模型结构\n",
        "        if print_model_structure:\n",
        "            print(\"\\n--- 开始打印 Stable Diffusion 模型结构 ---\")\n",
        "\n",
        "            if hasattr(pipe, 'vae') and pipe.vae is not None:\n",
        "                print(\"\\n1. VAE (Variational Autoencoder) 结构:\")\n",
        "                print(pipe.vae)\n",
        "            else:\n",
        "                print(\"\\n1. VAE: 未加载或不可用\")\n",
        "\n",
        "            if hasattr(pipe, 'text_encoder') and pipe.text_encoder is not None:\n",
        "                print(\"\\n2. Text Encoder 结构:\")\n",
        "                print(pipe.text_encoder)\n",
        "            else:\n",
        "                print(\"\\n2. Text Encoder: 未加载或不可用\")\n",
        "\n",
        "            if hasattr(pipe, 'tokenizer') and pipe.tokenizer is not None:\n",
        "                print(\"\\n3. Tokenizer 信息:\")\n",
        "                print(pipe.tokenizer) # Tokenizer 不是 nn.Module, 打印其类和配置\n",
        "            else:\n",
        "                print(\"\\n3. Tokenizer: 未加载或不可用\")\n",
        "\n",
        "            if hasattr(pipe, 'unet') and pipe.unet is not None:\n",
        "                print(\"\\n4. U-Net 结构:\")\n",
        "                print(pipe.unet)\n",
        "            else:\n",
        "                print(\"\\n4. U-Net: 未加载或不可用\")\n",
        "\n",
        "            if hasattr(pipe, 'scheduler') and pipe.scheduler is not None:\n",
        "                print(\"\\n5. Scheduler 信息:\")\n",
        "                print(pipe.scheduler) # Scheduler 不是 nn.Module, 打印其类和配置\n",
        "            else:\n",
        "                print(\"\\n5. Scheduler: 未加载或不可用\")\n",
        "\n",
        "            if hasattr(pipe, 'safety_checker') and pipe.safety_checker is not None:\n",
        "                print(\"\\n6. Safety Checker 结构:\")\n",
        "                print(pipe.safety_checker)\n",
        "            else:\n",
        "                print(\"\\n6. Safety Checker: 未加载或不可用\")\n",
        "\n",
        "            if hasattr(pipe, 'feature_extractor') and pipe.feature_extractor is not None:\n",
        "                print(\"\\n7. Feature Extractor (通常用于 Safety Checker) 信息:\")\n",
        "                print(pipe.feature_extractor) # 通常是 PreTrainedFeatureExtractor, 打印其类和配置\n",
        "            else:\n",
        "                print(\"\\n7. Feature Extractor: 未加载或不可用\")\n",
        "\n",
        "            print(\"\\n--- 模型结构打印完成 ---\\n\")\n",
        "\n",
        "        # 生成图像\n",
        "        # num_inference_steps 控制去噪步数，通常 50 步效果不错，增加步数可以提升细节但也会增加时间\n",
        "        # guidance_scale 控制提示与图像的符合程度，值越高越符合提示，但可能牺牲图像质量或多样性，通常 7-8.5 效果较好\n",
        "        print(\"正在生成图像...\")\n",
        "        with torch.no_grad(): # 在推理时禁用梯度计算以节省内存和加速\n",
        "            # 如果需要设置随机种子: generator = torch.Generator(device=device).manual_seed(42)\n",
        "            # image = pipe(prompt, num_inference_steps=50, guidance_scale=7.5, generator=generator).images[0]\n",
        "            image = pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]\n",
        "\n",
        "        print(\"图像生成完成。\")\n",
        "\n",
        "        # 保存图像\n",
        "        image.save(output_path)\n",
        "        print(f\"图像已保存到: {output_path}\")\n",
        "\n",
        "        # （可选）显示图像 (如果环境支持)\n",
        "        # try:\n",
        "        #     image.show()\n",
        "        # except Exception as e:\n",
        "        #     print(f\"无法显示图像: {e} (可能在无头服务器上运行)\")\n",
        "\n",
        "    except Exception as e:\n",
        "        print(f\"生成图像或打印模型结构时发生错误: {e}\")\n",
        "        if \"out of memory\" in str(e).lower():\n",
        "            print(\"显存不足 (Out of Memory)。请尝试以下方法：\")\n",
        "            print(\"1. 如果您有多个GPU，请确保 CUDA_VISIBLE_DEVICES 环境变量设置正确。\")\n",
        "            print(\"2. 尝试使用更小的模型（如果可用）。\")\n",
        "            print(\"3. 减小图像生成尺寸（如果 pipeline 支持 width/height 参数）。\")\n",
        "            print(\"4. 如果在 GPU 上运行，确保已使用 torch_dtype=torch.float16。\")\n",
        "            print(\"5. 尝试启用 'pipe.enable_vae_slicing()' 或 'pipe.enable_model_cpu_offload()'。\")\n",
        "            print(\"6. 关闭其他占用显存的程序。\")\n",
        "            print(\"7. 如果使用的是 CPU，确保有足够的 RAM。\")\n",
        "        elif \"requires a GPU\" in str(e).lower() and device == \"cpu\":\n",
        "             print(\"模型可能配置为仅在 GPU 上运行。请检查模型文档或尝试其他模型。\")\n",
        "\n",
        "\n",
        "if __name__ == \"__main__\":\n",
        "    # --- 用户配置 ---\n",
        "    # 你想要生成的图像的描述\n",
        "    # text_prompt = \"一只穿着宇航服的猫在月球上弹吉他，数字艺术风格\"\n",
        "    text_prompt = \"A photo of an astronaut riding a horse on the moon\" # 英文提示通常效果更好\n",
        "\n",
        "    # 生成图像的保存文件名\n",
        "    output_image_path = \"astronaut_cat_moon.png\"\n",
        "\n",
        "    # 选择模型 (更多模型请访问 https://huggingface.co/models?pipeline_tag=text-to-image&sort=downloads)\n",
        "    # model_name = \"runwayml/stable-diffusion-v1-5\"\n",
        "    model_name = \"stabilityai/stable-diffusion-2-1-base\" # 512x512 基础模型\n",
        "    # model_name = \"stabilityai/stable-diffusion-xl-base-1.0\" # SDXL 模型，质量更高，但需要更多资源\n",
        "                                                            # 使用 SDXL 时，pipeline 可能需要换成 StableDiffusionXLPipeline\n",
        "\n",
        "    # 选择设备 (\"cuda\" 或 \"cpu\")\n",
        "    # 如果您有 NVIDIA GPU 并且已正确安装 CUDA 和 PyTorch GPU 版本，请使用 \"cuda\"\n",
        "    # 否则，使用 \"cpu\" (会非常慢)\n",
        "    processing_device = \"cuda\"\n",
        "    # processing_device = \"cpu\"\n",
        "\n",
        "    # 是否打印模型结构\n",
        "    should_print_structure = True\n",
        "\n",
        "    # --- 执行 ---\n",
        "    generate_image_from_text(\n",
        "        prompt=text_prompt,\n",
        "        output_path=output_image_path,\n",
        "        model_id=model_name,\n",
        "        device=processing_device,\n",
        "        print_model_structure=should_print_structure # 传递参数以打印结构\n",
        "    )\n",
        "\n",
        "    # 如果只想打印模型结构而不生成图像，可以创建一个单独的函数或调整逻辑\n",
        "    # 例如:\n",
        "    # if should_print_structure:\n",
        "    #     # 只加载模型并打印结构，不进行图像生成\n",
        "    #     try:\n",
        "    #         pipe = StableDiffusionPipeline.from_pretrained(model_name, torch_dtype=torch.float16 if processing_device == \"cuda\" else torch.float32)\n",
        "    #         pipe = pipe.to(processing_device)\n",
        "    #         # ... (在此处复制打印结构的代码块) ...\n",
        "    #     except Exception as e:\n",
        "    #         print(f\"加载或打印模型结构时出错: {e}\")\n",
        "    # else:\n",
        "    #     # 正常生成图像\n",
        "    #     generate_image_from_text(\n",
        "    #         prompt=text_prompt,\n",
        "    #         output_path=output_image_path,\n",
        "    #         model_id=model_name,\n",
        "    #         device=processing_device,\n",
        "    #         print_model_structure=False\n",
        "    #     )\n",
        "\n",
        "\n",
        "    # --- 更多示例提示 ---\n",
        "    # generate_image_from_text(\"A futuristic cityscape at sunset, with flying cars and neon lights, concept art\", \"futuristic_city.png\", device=processing_device)\n",
        "    # generate_image_from_text(\"A hyperrealistic portrait of an old fisherman with a weathered face, detailed skin texture\", \"fisherman_portrait.png\", device=processing_device)\n",
        "    # generate_image_from_text(\"An oil painting of a mystical forest with glowing mushrooms and ancient trees, fantasy art\", \"mystical_forest.png\", device=processing_device)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "开始生成图像，提示: 'A photo of an astronaut riding a horse on the moon'\n",
            "使用模型: stabilityai/stable-diffusion-2-1-base\n",
            "使用设备: cuda\n",
            "正在加载模型... 这可能需要一些时间，特别是第一次运行时会自动下载模型。\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
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        {
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          "text": [
            "Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`\n",
            "WARNING:huggingface_hub.file_download:Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`\n"
          ]
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            "Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`\n",
            "WARNING:huggingface_hub.file_download:Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`\n"
          ]
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          "text": [
            "Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`\n",
            "WARNING:huggingface_hub.file_download:Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`\n"
          ]
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
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            ],
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        {
          "output_type": "stream",
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          "text": [
            "模型加载完成。\n",
            "\n",
            "--- 开始打印 Stable Diffusion 模型结构 ---\n",
            "\n",
            "1. VAE (Variational Autoencoder) 结构:\n",
            "AutoencoderKL(\n",
            "  (encoder): Encoder(\n",
            "    (conv_in): Conv2d(3, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (down_blocks): ModuleList(\n",
            "      (0): DownEncoderBlock2D(\n",
            "        (resnets): ModuleList(\n",
            "          (0-1): 2 x ResnetBlock2D(\n",
            "            (norm1): GroupNorm(32, 128, eps=1e-06, affine=True)\n",
            "            (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "            (norm2): GroupNorm(32, 128, eps=1e-06, affine=True)\n",
            "            (dropout): Dropout(p=0.0, inplace=False)\n",
            "            (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "            (nonlinearity): SiLU()\n",
            "          )\n",
            "        )\n",
            "        (downsamplers): ModuleList(\n",
            "          (0): Downsample2D(\n",
            "            (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2))\n",
            "          )\n",
            "        )\n",
            "      )\n",
            "      (1): DownEncoderBlock2D(\n",
            "        (resnets): ModuleList(\n",
            "          (0): ResnetBlock2D(\n",
            "            (norm1): GroupNorm(32, 128, eps=1e-06, affine=True)\n",
            "            (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "            (norm2): GroupNorm(32, 256, eps=1e-06, affine=True)\n",
            "            (dropout): Dropout(p=0.0, inplace=False)\n",
            "            (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "            (nonlinearity): SiLU()\n",
            "            (conv_shortcut): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1))\n",
            "          )\n",
            "          (1): ResnetBlock2D(\n",
            "            (norm1): GroupNorm(32, 256, eps=1e-06, affine=True)\n",
            "            (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "            (norm2): GroupNorm(32, 256, eps=1e-06, affine=True)\n",
            "            (dropout): Dropout(p=0.0, inplace=False)\n",
            "            (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "            (nonlinearity): SiLU()\n",
            "          )\n",
            "        )\n",
            "        (downsamplers): ModuleList(\n",
            "          (0): Downsample2D(\n",
            "            (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2))\n",
            "          )\n",
            "        )\n",
            "      )\n",
            "      (2): DownEncoderBlock2D(\n",
            "        (resnets): ModuleList(\n",
            "          (0): ResnetBlock2D(\n",
            "            (norm1): GroupNorm(32, 256, eps=1e-06, affine=True)\n",
            "            (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "            (norm2): GroupNorm(32, 512, eps=1e-06, affine=True)\n",
            "            (dropout): Dropout(p=0.0, inplace=False)\n",
            "            (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "            (nonlinearity): SiLU()\n",
            "            (conv_shortcut): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1))\n",
            "          )\n",
            "          (1): ResnetBlock2D(\n",
            "            (norm1): GroupNorm(32, 512, eps=1e-06, affine=True)\n",
            "            (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "            (norm2): GroupNorm(32, 512, eps=1e-06, affine=True)\n",
            "            (dropout): Dropout(p=0.0, inplace=False)\n",
            "            (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "            (nonlinearity): SiLU()\n",
            "          )\n",
            "        )\n",
            "        (downsamplers): ModuleList(\n",
            "          (0): Downsample2D(\n",
            "            (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2))\n",
            "          )\n",
            "        )\n",
            "      )\n",
            "      (3): DownEncoderBlock2D(\n",
            "        (resnets): ModuleList(\n",
            "          (0-1): 2 x ResnetBlock2D(\n",
            "            (norm1): GroupNorm(32, 512, eps=1e-06, affine=True)\n",
            "            (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "            (norm2): GroupNorm(32, 512, eps=1e-06, affine=True)\n",
            "            (dropout): Dropout(p=0.0, inplace=False)\n",
            "            (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "            (nonlinearity): SiLU()\n",
            "          )\n",
            "        )\n",
            "      )\n",
            "    )\n",
            "    (mid_block): UNetMidBlock2D(\n",
            "      (attentions): ModuleList(\n",
            "        (0): Attention(\n",
            "          (group_norm): GroupNorm(32, 512, eps=1e-06, affine=True)\n",
            "          (to_q): Linear(in_features=512, out_features=512, bias=True)\n",
            "          (to_k): Linear(in_features=512, out_features=512, bias=True)\n",
            "          (to_v): Linear(in_features=512, out_features=512, bias=True)\n",
            "          (to_out): ModuleList(\n",
            "            (0): Linear(in_features=512, out_features=512, bias=True)\n",
            "            (1): Dropout(p=0.0, inplace=False)\n",
            "          )\n",
            "        )\n",
            "      )\n",
            "      (resnets): ModuleList(\n",
            "        (0-1): 2 x ResnetBlock2D(\n",
            "          (norm1): GroupNorm(32, 512, eps=1e-06, affine=True)\n",
            "          (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "          (norm2): GroupNorm(32, 512, eps=1e-06, affine=True)\n",
            "          (dropout): Dropout(p=0.0, inplace=False)\n",
            "          (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "          (nonlinearity): SiLU()\n",
            "        )\n",
            "      )\n",
            "    )\n",
            "    (conv_norm_out): GroupNorm(32, 512, eps=1e-06, affine=True)\n",
            "    (conv_act): SiLU()\n",
            "    (conv_out): Conv2d(512, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "  )\n",
            "  (decoder): Decoder(\n",
            "    (conv_in): Conv2d(4, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (up_blocks): ModuleList(\n",
            "      (0-1): 2 x UpDecoderBlock2D(\n",
            "        (resnets): ModuleList(\n",
            "          (0-2): 3 x ResnetBlock2D(\n",
            "            (norm1): GroupNorm(32, 512, eps=1e-06, affine=True)\n",
            "            (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "            (norm2): GroupNorm(32, 512, eps=1e-06, affine=True)\n",
            "            (dropout): Dropout(p=0.0, inplace=False)\n",
            "            (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "            (nonlinearity): SiLU()\n",
            "          )\n",
            "        )\n",
            "        (upsamplers): ModuleList(\n",
            "          (0): Upsample2D(\n",
            "            (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "          )\n",
            "        )\n",
            "      )\n",
            "      (2): UpDecoderBlock2D(\n",
            "        (resnets): ModuleList(\n",
            "          (0): ResnetBlock2D(\n",
            "            (norm1): GroupNorm(32, 512, eps=1e-06, affine=True)\n",
            "            (conv1): Conv2d(512, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "            (norm2): GroupNorm(32, 256, eps=1e-06, affine=True)\n",
            "            (dropout): Dropout(p=0.0, inplace=False)\n",
            "            (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "            (nonlinearity): SiLU()\n",
            "            (conv_shortcut): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))\n",
            "          )\n",
            "          (1-2): 2 x ResnetBlock2D(\n",
            "            (norm1): GroupNorm(32, 256, eps=1e-06, affine=True)\n",
            "            (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "            (norm2): GroupNorm(32, 256, eps=1e-06, affine=True)\n",
            "            (dropout): Dropout(p=0.0, inplace=False)\n",
            "            (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "            (nonlinearity): SiLU()\n",
            "          )\n",
            "        )\n",
            "        (upsamplers): ModuleList(\n",
            "          (0): Upsample2D(\n",
            "            (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "          )\n",
            "        )\n",
            "      )\n",
            "      (3): UpDecoderBlock2D(\n",
            "        (resnets): ModuleList(\n",
            "          (0): ResnetBlock2D(\n",
            "            (norm1): GroupNorm(32, 256, eps=1e-06, affine=True)\n",
            "            (conv1): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "            (norm2): GroupNorm(32, 128, eps=1e-06, affine=True)\n",
            "            (dropout): Dropout(p=0.0, inplace=False)\n",
            "            (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "            (nonlinearity): SiLU()\n",
            "            (conv_shortcut): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))\n",
            "          )\n",
            "          (1-2): 2 x ResnetBlock2D(\n",
            "            (norm1): GroupNorm(32, 128, eps=1e-06, affine=True)\n",
            "            (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "            (norm2): GroupNorm(32, 128, eps=1e-06, affine=True)\n",
            "            (dropout): Dropout(p=0.0, inplace=False)\n",
            "            (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "            (nonlinearity): SiLU()\n",
            "          )\n",
            "        )\n",
            "      )\n",
            "    )\n",
            "    (mid_block): UNetMidBlock2D(\n",
            "      (attentions): ModuleList(\n",
            "        (0): Attention(\n",
            "          (group_norm): GroupNorm(32, 512, eps=1e-06, affine=True)\n",
            "          (to_q): Linear(in_features=512, out_features=512, bias=True)\n",
            "          (to_k): Linear(in_features=512, out_features=512, bias=True)\n",
            "          (to_v): Linear(in_features=512, out_features=512, bias=True)\n",
            "          (to_out): ModuleList(\n",
            "            (0): Linear(in_features=512, out_features=512, bias=True)\n",
            "            (1): Dropout(p=0.0, inplace=False)\n",
            "          )\n",
            "        )\n",
            "      )\n",
            "      (resnets): ModuleList(\n",
            "        (0-1): 2 x ResnetBlock2D(\n",
            "          (norm1): GroupNorm(32, 512, eps=1e-06, affine=True)\n",
            "          (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "          (norm2): GroupNorm(32, 512, eps=1e-06, affine=True)\n",
            "          (dropout): Dropout(p=0.0, inplace=False)\n",
            "          (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "          (nonlinearity): SiLU()\n",
            "        )\n",
            "      )\n",
            "    )\n",
            "    (conv_norm_out): GroupNorm(32, 128, eps=1e-06, affine=True)\n",
            "    (conv_act): SiLU()\n",
            "    (conv_out): Conv2d(128, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "  )\n",
            "  (quant_conv): Conv2d(8, 8, kernel_size=(1, 1), stride=(1, 1))\n",
            "  (post_quant_conv): Conv2d(4, 4, kernel_size=(1, 1), stride=(1, 1))\n",
            ")\n",
            "\n",
            "2. Text Encoder 结构:\n",
            "CLIPTextModel(\n",
            "  (text_model): CLIPTextTransformer(\n",
            "    (embeddings): CLIPTextEmbeddings(\n",
            "      (token_embedding): Embedding(49408, 1024)\n",
            "      (position_embedding): Embedding(77, 1024)\n",
            "    )\n",
            "    (encoder): CLIPEncoder(\n",
            "      (layers): ModuleList(\n",
            "        (0-22): 23 x CLIPEncoderLayer(\n",
            "          (self_attn): CLIPAttention(\n",
            "            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
            "            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
            "            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
            "            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
            "          )\n",
            "          (layer_norm1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
            "          (mlp): CLIPMLP(\n",
            "            (activation_fn): GELUActivation()\n",
            "            (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
            "            (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
            "          )\n",
            "          (layer_norm2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
            "        )\n",
            "      )\n",
            "    )\n",
            "    (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
            "  )\n",
            ")\n",
            "\n",
            "3. Tokenizer 信息:\n",
            "CLIPTokenizer(name_or_path='/root/.cache/huggingface/hub/models--stabilityai--stable-diffusion-2-1-base/snapshots/5ede9e4bf3e3fd1cb0ef2f7a3fff13ee514fdf06/tokenizer', vocab_size=49408, model_max_length=77, is_fast=False, padding_side='right', truncation_side='right', special_tokens={'bos_token': '<|startoftext|>', 'eos_token': '<|endoftext|>', 'unk_token': '<|endoftext|>', 'pad_token': '!'}, clean_up_tokenization_spaces=False, added_tokens_decoder={\n",
            "\t0: AddedToken(\"!\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
            "\t49406: AddedToken(\"<|startoftext|>\", rstrip=False, lstrip=False, single_word=False, normalized=True, special=True),\n",
            "\t49407: AddedToken(\"<|endoftext|>\", rstrip=False, lstrip=False, single_word=False, normalized=True, special=True),\n",
            "}\n",
            ")\n",
            "\n",
            "4. U-Net 结构:\n",
            "UNet2DConditionModel(\n",
            "  (conv_in): Conv2d(4, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "  (time_proj): Timesteps()\n",
            "  (time_embedding): TimestepEmbedding(\n",
            "    (linear_1): Linear(in_features=320, out_features=1280, bias=True)\n",
            "    (act): SiLU()\n",
            "    (linear_2): Linear(in_features=1280, out_features=1280, bias=True)\n",
            "  )\n",
            "  (down_blocks): ModuleList(\n",
            "    (0): CrossAttnDownBlock2D(\n",
            "      (attentions): ModuleList(\n",
            "        (0-1): 2 x Transformer2DModel(\n",
            "          (norm): GroupNorm(32, 320, eps=1e-06, affine=True)\n",
            "          (proj_in): Linear(in_features=320, out_features=320, bias=True)\n",
            "          (transformer_blocks): ModuleList(\n",
            "            (0): BasicTransformerBlock(\n",
            "              (norm1): LayerNorm((320,), eps=1e-05, elementwise_affine=True)\n",
            "              (attn1): Attention(\n",
            "                (to_q): Linear(in_features=320, out_features=320, bias=False)\n",
            "                (to_k): Linear(in_features=320, out_features=320, bias=False)\n",
            "                (to_v): Linear(in_features=320, out_features=320, bias=False)\n",
            "                (to_out): ModuleList(\n",
            "                  (0): Linear(in_features=320, out_features=320, bias=True)\n",
            "                  (1): Dropout(p=0.0, inplace=False)\n",
            "                )\n",
            "              )\n",
            "              (norm2): LayerNorm((320,), eps=1e-05, elementwise_affine=True)\n",
            "              (attn2): Attention(\n",
            "                (to_q): Linear(in_features=320, out_features=320, bias=False)\n",
            "                (to_k): Linear(in_features=1024, out_features=320, bias=False)\n",
            "                (to_v): Linear(in_features=1024, out_features=320, bias=False)\n",
            "                (to_out): ModuleList(\n",
            "                  (0): Linear(in_features=320, out_features=320, bias=True)\n",
            "                  (1): Dropout(p=0.0, inplace=False)\n",
            "                )\n",
            "              )\n",
            "              (norm3): LayerNorm((320,), eps=1e-05, elementwise_affine=True)\n",
            "              (ff): FeedForward(\n",
            "                (net): ModuleList(\n",
            "                  (0): GEGLU(\n",
            "                    (proj): Linear(in_features=320, out_features=2560, bias=True)\n",
            "                  )\n",
            "                  (1): Dropout(p=0.0, inplace=False)\n",
            "                  (2): Linear(in_features=1280, out_features=320, bias=True)\n",
            "                )\n",
            "              )\n",
            "            )\n",
            "          )\n",
            "          (proj_out): Linear(in_features=320, out_features=320, bias=True)\n",
            "        )\n",
            "      )\n",
            "      (resnets): ModuleList(\n",
            "        (0-1): 2 x ResnetBlock2D(\n",
            "          (norm1): GroupNorm(32, 320, eps=1e-05, affine=True)\n",
            "          (conv1): Conv2d(320, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "          (time_emb_proj): Linear(in_features=1280, out_features=320, bias=True)\n",
            "          (norm2): GroupNorm(32, 320, eps=1e-05, affine=True)\n",
            "          (dropout): Dropout(p=0.0, inplace=False)\n",
            "          (conv2): Conv2d(320, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "          (nonlinearity): SiLU()\n",
            "        )\n",
            "      )\n",
            "      (downsamplers): ModuleList(\n",
            "        (0): Downsample2D(\n",
            "          (conv): Conv2d(320, 320, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
            "        )\n",
            "      )\n",
            "    )\n",
            "    (1): CrossAttnDownBlock2D(\n",
            "      (attentions): ModuleList(\n",
            "        (0-1): 2 x Transformer2DModel(\n",
            "          (norm): GroupNorm(32, 640, eps=1e-06, affine=True)\n",
            "          (proj_in): Linear(in_features=640, out_features=640, bias=True)\n",
            "          (transformer_blocks): ModuleList(\n",
            "            (0): BasicTransformerBlock(\n",
            "              (norm1): LayerNorm((640,), eps=1e-05, elementwise_affine=True)\n",
            "              (attn1): Attention(\n",
            "                (to_q): Linear(in_features=640, out_features=640, bias=False)\n",
            "                (to_k): Linear(in_features=640, out_features=640, bias=False)\n",
            "                (to_v): Linear(in_features=640, out_features=640, bias=False)\n",
            "                (to_out): ModuleList(\n",
            "                  (0): Linear(in_features=640, out_features=640, bias=True)\n",
            "                  (1): Dropout(p=0.0, inplace=False)\n",
            "                )\n",
            "              )\n",
            "              (norm2): LayerNorm((640,), eps=1e-05, elementwise_affine=True)\n",
            "              (attn2): Attention(\n",
            "                (to_q): Linear(in_features=640, out_features=640, bias=False)\n",
            "                (to_k): Linear(in_features=1024, out_features=640, bias=False)\n",
            "                (to_v): Linear(in_features=1024, out_features=640, bias=False)\n",
            "                (to_out): ModuleList(\n",
            "                  (0): Linear(in_features=640, out_features=640, bias=True)\n",
            "                  (1): Dropout(p=0.0, inplace=False)\n",
            "                )\n",
            "              )\n",
            "              (norm3): LayerNorm((640,), eps=1e-05, elementwise_affine=True)\n",
            "              (ff): FeedForward(\n",
            "                (net): ModuleList(\n",
            "                  (0): GEGLU(\n",
            "                    (proj): Linear(in_features=640, out_features=5120, bias=True)\n",
            "                  )\n",
            "                  (1): Dropout(p=0.0, inplace=False)\n",
            "                  (2): Linear(in_features=2560, out_features=640, bias=True)\n",
            "                )\n",
            "              )\n",
            "            )\n",
            "          )\n",
            "          (proj_out): Linear(in_features=640, out_features=640, bias=True)\n",
            "        )\n",
            "      )\n",
            "      (resnets): ModuleList(\n",
            "        (0): ResnetBlock2D(\n",
            "          (norm1): GroupNorm(32, 320, eps=1e-05, affine=True)\n",
            "          (conv1): Conv2d(320, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "          (time_emb_proj): Linear(in_features=1280, out_features=640, bias=True)\n",
            "          (norm2): GroupNorm(32, 640, eps=1e-05, affine=True)\n",
            "          (dropout): Dropout(p=0.0, inplace=False)\n",
            "          (conv2): Conv2d(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "          (nonlinearity): SiLU()\n",
            "          (conv_shortcut): Conv2d(320, 640, kernel_size=(1, 1), stride=(1, 1))\n",
            "        )\n",
            "        (1): ResnetBlock2D(\n",
            "          (norm1): GroupNorm(32, 640, eps=1e-05, affine=True)\n",
            "          (conv1): Conv2d(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "          (time_emb_proj): Linear(in_features=1280, out_features=640, bias=True)\n",
            "          (norm2): GroupNorm(32, 640, eps=1e-05, affine=True)\n",
            "          (dropout): Dropout(p=0.0, inplace=False)\n",
            "          (conv2): Conv2d(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "          (nonlinearity): SiLU()\n",
            "        )\n",
            "      )\n",
            "      (downsamplers): ModuleList(\n",
            "        (0): Downsample2D(\n",
            "          (conv): Conv2d(640, 640, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
            "        )\n",
            "      )\n",
            "    )\n",
            "    (2): CrossAttnDownBlock2D(\n",
            "      (attentions): ModuleList(\n",
            "        (0-1): 2 x Transformer2DModel(\n",
            "          (norm): GroupNorm(32, 1280, eps=1e-06, affine=True)\n",
            "          (proj_in): Linear(in_features=1280, out_features=1280, bias=True)\n",
            "          (transformer_blocks): ModuleList(\n",
            "            (0): BasicTransformerBlock(\n",
            "              (norm1): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
            "              (attn1): Attention(\n",
            "                (to_q): Linear(in_features=1280, out_features=1280, bias=False)\n",
            "                (to_k): Linear(in_features=1280, out_features=1280, bias=False)\n",
            "                (to_v): Linear(in_features=1280, out_features=1280, bias=False)\n",
            "                (to_out): ModuleList(\n",
            "                  (0): Linear(in_features=1280, out_features=1280, bias=True)\n",
            "                  (1): Dropout(p=0.0, inplace=False)\n",
            "                )\n",
            "              )\n",
            "              (norm2): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
            "              (attn2): Attention(\n",
            "                (to_q): Linear(in_features=1280, out_features=1280, bias=False)\n",
            "                (to_k): Linear(in_features=1024, out_features=1280, bias=False)\n",
            "                (to_v): Linear(in_features=1024, out_features=1280, bias=False)\n",
            "                (to_out): ModuleList(\n",
            "                  (0): Linear(in_features=1280, out_features=1280, bias=True)\n",
            "                  (1): Dropout(p=0.0, inplace=False)\n",
            "                )\n",
            "              )\n",
            "              (norm3): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
            "              (ff): FeedForward(\n",
            "                (net): ModuleList(\n",
            "                  (0): GEGLU(\n",
            "                    (proj): Linear(in_features=1280, out_features=10240, bias=True)\n",
            "                  )\n",
            "                  (1): Dropout(p=0.0, inplace=False)\n",
            "                  (2): Linear(in_features=5120, out_features=1280, bias=True)\n",
            "                )\n",
            "              )\n",
            "            )\n",
            "          )\n",
            "          (proj_out): Linear(in_features=1280, out_features=1280, bias=True)\n",
            "        )\n",
            "      )\n",
            "      (resnets): ModuleList(\n",
            "        (0): ResnetBlock2D(\n",
            "          (norm1): GroupNorm(32, 640, eps=1e-05, affine=True)\n",
            "          (conv1): Conv2d(640, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "          (time_emb_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
            "          (norm2): GroupNorm(32, 1280, eps=1e-05, affine=True)\n",
            "          (dropout): Dropout(p=0.0, inplace=False)\n",
            "          (conv2): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "          (nonlinearity): SiLU()\n",
            "          (conv_shortcut): Conv2d(640, 1280, kernel_size=(1, 1), stride=(1, 1))\n",
            "        )\n",
            "        (1): ResnetBlock2D(\n",
            "          (norm1): GroupNorm(32, 1280, eps=1e-05, affine=True)\n",
            "          (conv1): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "          (time_emb_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
            "          (norm2): GroupNorm(32, 1280, eps=1e-05, affine=True)\n",
            "          (dropout): Dropout(p=0.0, inplace=False)\n",
            "          (conv2): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "          (nonlinearity): SiLU()\n",
            "        )\n",
            "      )\n",
            "      (downsamplers): ModuleList(\n",
            "        (0): Downsample2D(\n",
            "          (conv): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
            "        )\n",
            "      )\n",
            "    )\n",
            "    (3): DownBlock2D(\n",
            "      (resnets): ModuleList(\n",
            "        (0-1): 2 x ResnetBlock2D(\n",
            "          (norm1): GroupNorm(32, 1280, eps=1e-05, affine=True)\n",
            "          (conv1): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "          (time_emb_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
            "          (norm2): GroupNorm(32, 1280, eps=1e-05, affine=True)\n",
            "          (dropout): Dropout(p=0.0, inplace=False)\n",
            "          (conv2): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "          (nonlinearity): SiLU()\n",
            "        )\n",
            "      )\n",
            "    )\n",
            "  )\n",
            "  (up_blocks): ModuleList(\n",
            "    (0): UpBlock2D(\n",
            "      (resnets): ModuleList(\n",
            "        (0-2): 3 x ResnetBlock2D(\n",
            "          (norm1): GroupNorm(32, 2560, eps=1e-05, affine=True)\n",
            "          (conv1): Conv2d(2560, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "          (time_emb_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
            "          (norm2): GroupNorm(32, 1280, eps=1e-05, affine=True)\n",
            "          (dropout): Dropout(p=0.0, inplace=False)\n",
            "          (conv2): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "          (nonlinearity): SiLU()\n",
            "          (conv_shortcut): Conv2d(2560, 1280, kernel_size=(1, 1), stride=(1, 1))\n",
            "        )\n",
            "      )\n",
            "      (upsamplers): ModuleList(\n",
            "        (0): Upsample2D(\n",
            "          (conv): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        )\n",
            "      )\n",
            "    )\n",
            "    (1): CrossAttnUpBlock2D(\n",
            "      (attentions): ModuleList(\n",
            "        (0-2): 3 x Transformer2DModel(\n",
            "          (norm): GroupNorm(32, 1280, eps=1e-06, affine=True)\n",
            "          (proj_in): Linear(in_features=1280, out_features=1280, bias=True)\n",
            "          (transformer_blocks): ModuleList(\n",
            "            (0): BasicTransformerBlock(\n",
            "              (norm1): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
            "              (attn1): Attention(\n",
            "                (to_q): Linear(in_features=1280, out_features=1280, bias=False)\n",
            "                (to_k): Linear(in_features=1280, out_features=1280, bias=False)\n",
            "                (to_v): Linear(in_features=1280, out_features=1280, bias=False)\n",
            "                (to_out): ModuleList(\n",
            "                  (0): Linear(in_features=1280, out_features=1280, bias=True)\n",
            "                  (1): Dropout(p=0.0, inplace=False)\n",
            "                )\n",
            "              )\n",
            "              (norm2): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
            "              (attn2): Attention(\n",
            "                (to_q): Linear(in_features=1280, out_features=1280, bias=False)\n",
            "                (to_k): Linear(in_features=1024, out_features=1280, bias=False)\n",
            "                (to_v): Linear(in_features=1024, out_features=1280, bias=False)\n",
            "                (to_out): ModuleList(\n",
            "                  (0): Linear(in_features=1280, out_features=1280, bias=True)\n",
            "                  (1): Dropout(p=0.0, inplace=False)\n",
            "                )\n",
            "              )\n",
            "              (norm3): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
            "              (ff): FeedForward(\n",
            "                (net): ModuleList(\n",
            "                  (0): GEGLU(\n",
            "                    (proj): Linear(in_features=1280, out_features=10240, bias=True)\n",
            "                  )\n",
            "                  (1): Dropout(p=0.0, inplace=False)\n",
            "                  (2): Linear(in_features=5120, out_features=1280, bias=True)\n",
            "                )\n",
            "              )\n",
            "            )\n",
            "          )\n",
            "          (proj_out): Linear(in_features=1280, out_features=1280, bias=True)\n",
            "        )\n",
            "      )\n",
            "      (resnets): ModuleList(\n",
            "        (0-1): 2 x ResnetBlock2D(\n",
            "          (norm1): GroupNorm(32, 2560, eps=1e-05, affine=True)\n",
            "          (conv1): Conv2d(2560, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "          (time_emb_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
            "          (norm2): GroupNorm(32, 1280, eps=1e-05, affine=True)\n",
            "          (dropout): Dropout(p=0.0, inplace=False)\n",
            "          (conv2): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "          (nonlinearity): SiLU()\n",
            "          (conv_shortcut): Conv2d(2560, 1280, kernel_size=(1, 1), stride=(1, 1))\n",
            "        )\n",
            "        (2): ResnetBlock2D(\n",
            "          (norm1): GroupNorm(32, 1920, eps=1e-05, affine=True)\n",
            "          (conv1): Conv2d(1920, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "          (time_emb_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
            "          (norm2): GroupNorm(32, 1280, eps=1e-05, affine=True)\n",
            "          (dropout): Dropout(p=0.0, inplace=False)\n",
            "          (conv2): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "          (nonlinearity): SiLU()\n",
            "          (conv_shortcut): Conv2d(1920, 1280, kernel_size=(1, 1), stride=(1, 1))\n",
            "        )\n",
            "      )\n",
            "      (upsamplers): ModuleList(\n",
            "        (0): Upsample2D(\n",
            "          (conv): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        )\n",
            "      )\n",
            "    )\n",
            "    (2): CrossAttnUpBlock2D(\n",
            "      (attentions): ModuleList(\n",
            "        (0-2): 3 x Transformer2DModel(\n",
            "          (norm): GroupNorm(32, 640, eps=1e-06, affine=True)\n",
            "          (proj_in): Linear(in_features=640, out_features=640, bias=True)\n",
            "          (transformer_blocks): ModuleList(\n",
            "            (0): BasicTransformerBlock(\n",
            "              (norm1): LayerNorm((640,), eps=1e-05, elementwise_affine=True)\n",
            "              (attn1): Attention(\n",
            "                (to_q): Linear(in_features=640, out_features=640, bias=False)\n",
            "                (to_k): Linear(in_features=640, out_features=640, bias=False)\n",
            "                (to_v): Linear(in_features=640, out_features=640, bias=False)\n",
            "                (to_out): ModuleList(\n",
            "                  (0): Linear(in_features=640, out_features=640, bias=True)\n",
            "                  (1): Dropout(p=0.0, inplace=False)\n",
            "                )\n",
            "              )\n",
            "              (norm2): LayerNorm((640,), eps=1e-05, elementwise_affine=True)\n",
            "              (attn2): Attention(\n",
            "                (to_q): Linear(in_features=640, out_features=640, bias=False)\n",
            "                (to_k): Linear(in_features=1024, out_features=640, bias=False)\n",
            "                (to_v): Linear(in_features=1024, out_features=640, bias=False)\n",
            "                (to_out): ModuleList(\n",
            "                  (0): Linear(in_features=640, out_features=640, bias=True)\n",
            "                  (1): Dropout(p=0.0, inplace=False)\n",
            "                )\n",
            "              )\n",
            "              (norm3): LayerNorm((640,), eps=1e-05, elementwise_affine=True)\n",
            "              (ff): FeedForward(\n",
            "                (net): ModuleList(\n",
            "                  (0): GEGLU(\n",
            "                    (proj): Linear(in_features=640, out_features=5120, bias=True)\n",
            "                  )\n",
            "                  (1): Dropout(p=0.0, inplace=False)\n",
            "                  (2): Linear(in_features=2560, out_features=640, bias=True)\n",
            "                )\n",
            "              )\n",
            "            )\n",
            "          )\n",
            "          (proj_out): Linear(in_features=640, out_features=640, bias=True)\n",
            "        )\n",
            "      )\n",
            "      (resnets): ModuleList(\n",
            "        (0): ResnetBlock2D(\n",
            "          (norm1): GroupNorm(32, 1920, eps=1e-05, affine=True)\n",
            "          (conv1): Conv2d(1920, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "          (time_emb_proj): Linear(in_features=1280, out_features=640, bias=True)\n",
            "          (norm2): GroupNorm(32, 640, eps=1e-05, affine=True)\n",
            "          (dropout): Dropout(p=0.0, inplace=False)\n",
            "          (conv2): Conv2d(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "          (nonlinearity): SiLU()\n",
            "          (conv_shortcut): Conv2d(1920, 640, kernel_size=(1, 1), stride=(1, 1))\n",
            "        )\n",
            "        (1): ResnetBlock2D(\n",
            "          (norm1): GroupNorm(32, 1280, eps=1e-05, affine=True)\n",
            "          (conv1): Conv2d(1280, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "          (time_emb_proj): Linear(in_features=1280, out_features=640, bias=True)\n",
            "          (norm2): GroupNorm(32, 640, eps=1e-05, affine=True)\n",
            "          (dropout): Dropout(p=0.0, inplace=False)\n",
            "          (conv2): Conv2d(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "          (nonlinearity): SiLU()\n",
            "          (conv_shortcut): Conv2d(1280, 640, kernel_size=(1, 1), stride=(1, 1))\n",
            "        )\n",
            "        (2): ResnetBlock2D(\n",
            "          (norm1): GroupNorm(32, 960, eps=1e-05, affine=True)\n",
            "          (conv1): Conv2d(960, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "          (time_emb_proj): Linear(in_features=1280, out_features=640, bias=True)\n",
            "          (norm2): GroupNorm(32, 640, eps=1e-05, affine=True)\n",
            "          (dropout): Dropout(p=0.0, inplace=False)\n",
            "          (conv2): Conv2d(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "          (nonlinearity): SiLU()\n",
            "          (conv_shortcut): Conv2d(960, 640, kernel_size=(1, 1), stride=(1, 1))\n",
            "        )\n",
            "      )\n",
            "      (upsamplers): ModuleList(\n",
            "        (0): Upsample2D(\n",
            "          (conv): Conv2d(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        )\n",
            "      )\n",
            "    )\n",
            "    (3): CrossAttnUpBlock2D(\n",
            "      (attentions): ModuleList(\n",
            "        (0-2): 3 x Transformer2DModel(\n",
            "          (norm): GroupNorm(32, 320, eps=1e-06, affine=True)\n",
            "          (proj_in): Linear(in_features=320, out_features=320, bias=True)\n",
            "          (transformer_blocks): ModuleList(\n",
            "            (0): BasicTransformerBlock(\n",
            "              (norm1): LayerNorm((320,), eps=1e-05, elementwise_affine=True)\n",
            "              (attn1): Attention(\n",
            "                (to_q): Linear(in_features=320, out_features=320, bias=False)\n",
            "                (to_k): Linear(in_features=320, out_features=320, bias=False)\n",
            "                (to_v): Linear(in_features=320, out_features=320, bias=False)\n",
            "                (to_out): ModuleList(\n",
            "                  (0): Linear(in_features=320, out_features=320, bias=True)\n",
            "                  (1): Dropout(p=0.0, inplace=False)\n",
            "                )\n",
            "              )\n",
            "              (norm2): LayerNorm((320,), eps=1e-05, elementwise_affine=True)\n",
            "              (attn2): Attention(\n",
            "                (to_q): Linear(in_features=320, out_features=320, bias=False)\n",
            "                (to_k): Linear(in_features=1024, out_features=320, bias=False)\n",
            "                (to_v): Linear(in_features=1024, out_features=320, bias=False)\n",
            "                (to_out): ModuleList(\n",
            "                  (0): Linear(in_features=320, out_features=320, bias=True)\n",
            "                  (1): Dropout(p=0.0, inplace=False)\n",
            "                )\n",
            "              )\n",
            "              (norm3): LayerNorm((320,), eps=1e-05, elementwise_affine=True)\n",
            "              (ff): FeedForward(\n",
            "                (net): ModuleList(\n",
            "                  (0): GEGLU(\n",
            "                    (proj): Linear(in_features=320, out_features=2560, bias=True)\n",
            "                  )\n",
            "                  (1): Dropout(p=0.0, inplace=False)\n",
            "                  (2): Linear(in_features=1280, out_features=320, bias=True)\n",
            "                )\n",
            "              )\n",
            "            )\n",
            "          )\n",
            "          (proj_out): Linear(in_features=320, out_features=320, bias=True)\n",
            "        )\n",
            "      )\n",
            "      (resnets): ModuleList(\n",
            "        (0): ResnetBlock2D(\n",
            "          (norm1): GroupNorm(32, 960, eps=1e-05, affine=True)\n",
            "          (conv1): Conv2d(960, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "          (time_emb_proj): Linear(in_features=1280, out_features=320, bias=True)\n",
            "          (norm2): GroupNorm(32, 320, eps=1e-05, affine=True)\n",
            "          (dropout): Dropout(p=0.0, inplace=False)\n",
            "          (conv2): Conv2d(320, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "          (nonlinearity): SiLU()\n",
            "          (conv_shortcut): Conv2d(960, 320, kernel_size=(1, 1), stride=(1, 1))\n",
            "        )\n",
            "        (1-2): 2 x ResnetBlock2D(\n",
            "          (norm1): GroupNorm(32, 640, eps=1e-05, affine=True)\n",
            "          (conv1): Conv2d(640, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "          (time_emb_proj): Linear(in_features=1280, out_features=320, bias=True)\n",
            "          (norm2): GroupNorm(32, 320, eps=1e-05, affine=True)\n",
            "          (dropout): Dropout(p=0.0, inplace=False)\n",
            "          (conv2): Conv2d(320, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "          (nonlinearity): SiLU()\n",
            "          (conv_shortcut): Conv2d(640, 320, kernel_size=(1, 1), stride=(1, 1))\n",
            "        )\n",
            "      )\n",
            "    )\n",
            "  )\n",
            "  (mid_block): UNetMidBlock2DCrossAttn(\n",
            "    (attentions): ModuleList(\n",
            "      (0): Transformer2DModel(\n",
            "        (norm): GroupNorm(32, 1280, eps=1e-06, affine=True)\n",
            "        (proj_in): Linear(in_features=1280, out_features=1280, bias=True)\n",
            "        (transformer_blocks): ModuleList(\n",
            "          (0): BasicTransformerBlock(\n",
            "            (norm1): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
            "            (attn1): Attention(\n",
            "              (to_q): Linear(in_features=1280, out_features=1280, bias=False)\n",
            "              (to_k): Linear(in_features=1280, out_features=1280, bias=False)\n",
            "              (to_v): Linear(in_features=1280, out_features=1280, bias=False)\n",
            "              (to_out): ModuleList(\n",
            "                (0): Linear(in_features=1280, out_features=1280, bias=True)\n",
            "                (1): Dropout(p=0.0, inplace=False)\n",
            "              )\n",
            "            )\n",
            "            (norm2): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
            "            (attn2): Attention(\n",
            "              (to_q): Linear(in_features=1280, out_features=1280, bias=False)\n",
            "              (to_k): Linear(in_features=1024, out_features=1280, bias=False)\n",
            "              (to_v): Linear(in_features=1024, out_features=1280, bias=False)\n",
            "              (to_out): ModuleList(\n",
            "                (0): Linear(in_features=1280, out_features=1280, bias=True)\n",
            "                (1): Dropout(p=0.0, inplace=False)\n",
            "              )\n",
            "            )\n",
            "            (norm3): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
            "            (ff): FeedForward(\n",
            "              (net): ModuleList(\n",
            "                (0): GEGLU(\n",
            "                  (proj): Linear(in_features=1280, out_features=10240, bias=True)\n",
            "                )\n",
            "                (1): Dropout(p=0.0, inplace=False)\n",
            "                (2): Linear(in_features=5120, out_features=1280, bias=True)\n",
            "              )\n",
            "            )\n",
            "          )\n",
            "        )\n",
            "        (proj_out): Linear(in_features=1280, out_features=1280, bias=True)\n",
            "      )\n",
            "    )\n",
            "    (resnets): ModuleList(\n",
            "      (0-1): 2 x ResnetBlock2D(\n",
            "        (norm1): GroupNorm(32, 1280, eps=1e-05, affine=True)\n",
            "        (conv1): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (time_emb_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
            "        (norm2): GroupNorm(32, 1280, eps=1e-05, affine=True)\n",
            "        (dropout): Dropout(p=0.0, inplace=False)\n",
            "        (conv2): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (nonlinearity): SiLU()\n",
            "      )\n",
            "    )\n",
            "  )\n",
            "  (conv_norm_out): GroupNorm(32, 320, eps=1e-05, affine=True)\n",
            "  (conv_act): SiLU()\n",
            "  (conv_out): Conv2d(320, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            ")\n",
            "\n",
            "5. Scheduler 信息:\n",
            "PNDMScheduler {\n",
            "  \"_class_name\": \"PNDMScheduler\",\n",
            "  \"_diffusers_version\": \"0.33.1\",\n",
            "  \"beta_end\": 0.012,\n",
            "  \"beta_schedule\": \"scaled_linear\",\n",
            "  \"beta_start\": 0.00085,\n",
            "  \"clip_sample\": false,\n",
            "  \"num_train_timesteps\": 1000,\n",
            "  \"prediction_type\": \"epsilon\",\n",
            "  \"set_alpha_to_one\": false,\n",
            "  \"skip_prk_steps\": true,\n",
            "  \"steps_offset\": 1,\n",
            "  \"timestep_spacing\": \"leading\",\n",
            "  \"trained_betas\": null\n",
            "}\n",
            "\n",
            "\n",
            "6. Safety Checker: 未加载或不可用\n",
            "\n",
            "7. Feature Extractor (通常用于 Safety Checker) 信息:\n",
            "CLIPImageProcessor {\n",
            "  \"crop_size\": {\n",
            "    \"height\": 224,\n",
            "    \"width\": 224\n",
            "  },\n",
            "  \"do_center_crop\": true,\n",
            "  \"do_convert_rgb\": true,\n",
            "  \"do_normalize\": true,\n",
            "  \"do_rescale\": true,\n",
            "  \"do_resize\": true,\n",
            "  \"image_mean\": [\n",
            "    0.48145466,\n",
            "    0.4578275,\n",
            "    0.40821073\n",
            "  ],\n",
            "  \"image_processor_type\": \"CLIPImageProcessor\",\n",
            "  \"image_std\": [\n",
            "    0.26862954,\n",
            "    0.26130258,\n",
            "    0.27577711\n",
            "  ],\n",
            "  \"resample\": 3,\n",
            "  \"rescale_factor\": 0.00392156862745098,\n",
            "  \"size\": {\n",
            "    \"shortest_edge\": 224\n",
            "  }\n",
            "}\n",
            "\n",
            "\n",
            "--- 模型结构打印完成 ---\n",
            "\n",
            "正在生成图像...\n"
          ]
        },
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          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/50 [00:00<?, ?it/s]"
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          "text": [
            "图像生成完成。\n",
            "图像已保存到: astronaut_cat_moon.png\n"
          ]
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